Coopermerrill1280
Spectrophotometry is an indirect non-invasive and quantitative method for specifying materials with unknown contents based on absorption behavior. This paper presents the first application of artificial neural network in spectrophotometry for quantification of human sperm concentration. A well-trained full spectrum neural network (FSNN) model is developed by examining the absorption response of sperm samples from 41 human subjects to different light spectra (wavelength from 390 to 1100 nm). It is shown that this FSNN accurately estimates sperm concentration based on the full absorption spectrum with over 93% prediction accuracy, and provides 100% agreement with clinical assessments in differentiating the samples of healthy donor from patient samples. We suggest the machine learning-based spectrophotometry approach with the trained FSNN model as a rapid, low-cost, and powerful technique to quantify sperm concentration. The performance of this technique is superior to available spectrophotometry methods currently used for semen analysis and will provide novel research and clinical opportunities for tackling male infertility.Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of many people around the world and is associated with a five-fold increased risk of stroke and mortality. Like other problems in the healthcare domain, artificial intelligence (AI)-based models have been used to detect AF from patients' ECG signals. The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability. In other words, these approaches are unable to explain the reasons behind their decisions. The lack of interpretability is a common challenge toward a wide application of machine learning (ML)-based approaches in the healthcare which limits the trust of clinicians in such methods. To address this challenge, we propose HAN-ECG, an interpretable bidirectional-recurrent-neural-network-based approach for the AF detection task. The HAN-ECG employs three attention mechanism levels to provide a multi-resolution analysis of the patterns in ECG leading to AF. The detected patterns by this hierarchical attention model facilitate the interpretation of the neural network decision process in identifying the patterns in the signal which contributed the most to the final detection. Experimental results on two AF databases demonstrate that our proposed model performs better than the existing algorithms. Visualization of these attention layers illustrates that our proposed model decides upon the important waves and heartbeats which are clinically meaningful in the detection task (e.g., absence of P-waves, and irregular R-R intervals for the AF detection task).Histopathology of Hematoxylin and Eosin (H&E)-stained tissue obtained from biopsy is commonly used in prostate cancer (PCa) diagnosis. Automatic PCa classification of digitized H&E slides has been developed before, but no attempts have been made to classify PCa using additional tissue stains registered to H&E. In this paper, we demonstrate that using H&E, Ki67 and p63-stained (3-stain) tissue improves PCa classification relative to H&E alone. We also show that we can infer PCa-relevant Ki67 and p63 information from the H&E slides alone, and use it to achieve H&E-based PCa classification that is comparable to the 3-stain classification. Reported improvements apply to classifying benign vs. malignant tissue, and low grade (Gleason group 2) vs. high grade (Gleason groups 3,4,5) cancer. Specifically, we conducted four classification tasks using 333 tissue samples extracted from 231 radical prostatectomy patients regression tree-based classification using either (i) 3-stain features, with a benign vs malignant area under the curve (AUC = 92.9%), or (ii) real H&E features and H&E features learned from Ki67 and p63 stains (AUC = 92.4%), as well as deep learning classification using either (iii) real 3-stain tissue patches (AUC = 94.3%) and (iv) real H&E patches and generated Ki67 and p63 patches (AUC = 93.0%) using a deep convolutional generative adversarial network. Classification performance was assessed with Monte Carlo cross validation and quantified in terms of the Area Under the Curve, Brier score, sensitivity, and specificity. Our results are interpretable and indicate that the standard H&E classification could be improved by mimicking other stain types.
The objective of this study was to evaluate out-of-field dose distribution calculation accuracy by the Anisotropic Analytical Algorithm (AAA), version 13.0.26, in Eclipse TPS, (Varian Medical Systems, Palo Alto, Ca, USA) for sliding window IMRT delivery technique in prostate cancer patients.
Prostate IMRT plans with nine coplanar were calculated with the AAA Eclipse treatment planning system. To assess the accuracy of dose calculation predicted by the Eclipse in normal tissue and OARs located out of radiation field areas, including the rectum, bladder, right and left head of the femur, absolute organ dose value, and dose distribution were measured using the Delta
IMRT phantom.
In the out-of-field areas, underestimation of -0.66% in organs near the field edge to -39.63% in organs far from the field edge (2.5 and 7.3cm respectively) occurred in the TPS calculations. The percentage of dose deviation for the femoral heads was 95.7 on average while for the organ closer to the target (rectum) it was 79.81.
AAA dosimetry algorithm (used in Eclipse TPS) showed poor dose calculation in areas beyond the treatment fields border where underestimation varies with the distance from the field edges. A significant underestimation was found for the AAA algorithm in the sliding window IMRT technique (P-value>0.05).
0.05).Long noncoding RNAs (lncRNAs) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. They are a divergent group of RNAs and are easily differentiated from other types with unique characteristics, functions, and mechanisms of action. In this review, we provide a list of some of the prominent data repositories containing lncRNAs, their interactome, and predicted and validated disease associations. Next, we discuss various wet-lab experiments formulated to obtain the data for these repositories. We also provide a critical review of in silico methods available for the identification purpose and suggest techniques to further improve their performance. The bulk of the methods currently focus on distinguishing lncRNA transcripts from the coding ones. Functional annotation of these transcripts still remains a grey area and more efforts are needed in that space. Finally, we provide details of current progress, discuss impediments, and illustrate a roadmap for developing a generalized computational pipeline for comprehensive annotation of lncRNAs, which is essential to accelerate research in this area.
Osteoporosis is a systemic skeletal disease that leads to a high risk for bone fractures. Morinda officinalis How. has been used as osteoporosis treatment in China. However, its mechanism of action as an anti-osteoporotic herb remains unknown.
A network pharmacology approach was applied to explore the potential mechanisms of action of M. officinalis in osteoporosis treatment. The active compounds of M. officinalis and their potential osteoporosis-related targets were retrieved from TCMSP, TCMID, SwissTargetPrediction, DrugBank, DisGeNET, GeneCards, OMIM, and TTD databases. A protein-protein interaction network was built to analyze the target interactions. The Metascape database was used to carry out GO enrichment analysis and KEGG pathway analysis. Moreover, interactions between active compounds and potential targets were investigated through molecular docking.
A total of 17 active compounds and 93 anti-osteoporosis targets of M. officinalis were selected for analysis. Tyrphostin The GO enrichment analysis resultsdes new insights into the development of a natural therapy for the prevention and treatment of osteoporosis.To develop elastography imaging technologies and implement image reconstruction algorithms, testing is done with phantoms. Although the validation step is usually taken using real data and physical phantoms, their geometry as well as composition, biomechanical parameters, and details of applying stress cannot be modified readily. Such considerations have gained increasing importance with the growth of elastography techniques as one of the non-invasive medical imaging modalities, which can map the elastic properties and stiffness of soft tissues. In this article, we develop a digital viscoelastic phantom using computed tomography (CT) imaging data and several application software tools based on illustrations of normal liver anatomy so as to investigate the biomechanics of elastography via finite element modeling (FEM). Here we discuss how to create this phantom step by step, demonstrate typical shear wave elastography (SWE) experiments of applying transient stress to the liver model, and calculate quantitative measurements. In particular, shear wave velocities are investigated through a parametric study designed based on tissue stiffness and distance from the applied stress. According to the results of FEM analysis, low errors were obtained for shear wave velocity estimation for both mechanical stress (~2-5%) and acoustic radiation force (~3-7%). Results show that our model is a powerful framework and benchmark for simulating and implementing different algorithms in shear wave elastography, which can serve as a guide for upcoming researches and assist scientists to optimize their subsequent experiments in terms of design.
The impact of COVID-19 upon acute care admission rates and patterns are unknown. We sought to determine the change in rates and types of admissions to tertiary and specialty care hospitals in the COVID-19 era compared with pre-COVID-19 era.
Acute care admissions to the largest tertiary care referral hospital, designated national referral centers for cardiac, cancer and maternity hospital in the State of Qatar during March 2020 (COVID-19 era) and January 2020 and March 2019 (pre-COVID-19 era) were compared. We calculated total admissions, admissions for eight specific acute care conditions, in-hospital mortality rate, and length of stay at each hospital.
A total of 18,889 hospital admissions were recorded. A sharp decline ranging from 9% to 75% was observed in overall admissions. A decline in both elective and non-elective surgeries was observed. A decline of 9%-58% was observed in admissions for acute appendicitis, acute coronary syndrome, stroke, bone fractures, cancer, and live births, whereas an increase in admissions due to respiratory tract infections was observed. Overall length of stay was shorter in the COVID-19 period possibly suggesting lesser overall disease severity, with no significant change in in-hospital mortality. Unadjusted mortality rate for Qatar showed marginal increase in the COVID-19 period.
We observed a sharp decline in acute care hospital admissions, with a significant decline in admissions due to seven out of eight acute care conditions. This decline was associated with a shorter length of stay but not associated with a change in in-hospital mortality rate.
We observed a sharp decline in acute care hospital admissions, with a significant decline in admissions due to seven out of eight acute care conditions. This decline was associated with a shorter length of stay but not associated with a change in in-hospital mortality rate.
